Software Reliability Model Selection Based on Deep Learning with Application to the Optimal Release Problem
Yoshinobu Tamura, Shigeru Yamada · Journal of Industrial Engineering and Management Science · 2016
In the past, many software reliability models have been proposed by several researchers.Also, several model selection criteria such as Akaike's information criterion, mean square errors, predicted relative error and so on, have been used for the selection of optimal software reliability models.These assessment criteria can be useful for the software managers to assess the past trend of fault data.However, it is very important to assess the prediction accuracy of model after the end of fault data observation in the actual software project.In this paper, we propose a method of optimal software reliability model selection based on the deep learning.Moreover, we show several numerical examples of software reliability assessment in the actual software projects.In particular, we discuss the optimal release time and total expected software cost in terms of the model selection based on the deep learning.